MétaCan
Menu
Back to cohort
Record W4320727869 · doi:10.3390/children10020370

Do Teachers Question the Reality of Pain in Their Students? A Survey Using the Concept of Pain Inventory-Proxy (COPI-Proxy)

2023· article· en· W4320727869 on OpenAlexaff
Rebecca Fechner, Mélanie Noël, Arianne P. Verhagen, Erin Turbitt, Joshua W. Pate

Bibliographic record

VenueChildren · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsOntario Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersDepartment of Education and TrainingUniversity of Technology SydneyAustralian Government
KeywordsVignettePsychologyProxy (statistics)Convergent validityCronbach's alphaSocial psychologyClinical psychologyPsychometricsComputer scienceInternal consistency

Abstract

fetched live from OpenAlex

An assessment of a teacher's concept of their student's pain could be useful to guide preventative and targeted school-based pain science education. We aimed to assess a teacher's own concept of pain against their concept of their student's pain and examine the psychometric properties of the tool. Teachers of 10-12-year-old children were invited to participate in an online survey via social media. We modified the Concept of Pain Inventory (COPI) by inserting a vignette (COPI-Proxy), and we included questions to explore teacher stigma. Overall, a sample of 233 teachers participated in the survey. The COPI-Proxy scores showed that teachers can conceptualize their student's pain separately but are influenced by their own beliefs. Only 76% affirmed the pain in the vignette as real. Teachers used potentially stigmatizing language to describe pain in their survey responses. The COPI-Proxy had acceptable internal consistency (Cronbach's alpha = 0.72) and moderate convergent validity with the COPI (r = 0.56). The results show the potential benefit of the COPI-Proxy for assessing someone's concept of another's pain, particularly for teachers who are important social influencers of children.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.345
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueChildrenSame topicPediatric Pain Management TechniquesFrench-language works237,207